Researchers Propose Principled Method for Converting Privacy Parameters in Gaussian Differential Privacy
A new arXiv paper provides mappings to convert traditional differential privacy parameters (pure-DP ε) to Gaussian differential privacy (GDP) parameters (μ) by analyzing membership inference attacks. The work addresses a practical gap in privacy-preserving machine learning by establishing how to translate between two different privacy frameworks. This matters because it enables researchers to more accurately report and compare privacy guarantees across different machine learning systems.
Researchers have published a technical paper on arXiv proposing principled methods for converting privacy parameters between two differential privacy frameworks used in machine learning. The work matches worst-case adversary success rates under membership inference attacks across three different metrics: multiplicative advantage at fixed false positive rate, precision at fixed recall, and standard privacy profiles. Based on their analysis, the authors recommend μ ≈ ε/5 as a conservative general-purpose conversion rule and provide tabulated μ values across a range of useful parameters. This contribution addresses a practical challenge in privacy-preserving machine learning, where researchers need to accurately translate between pure differential privacy (DP) and Gaussian differential privacy (GDP) frameworks when reporting privacy guarantees.
What's missing
The paper's own limitations and open questions are not detailed in the abstract provided. Specifically, the scope of applicability of the μ ≈ ε/5 recommendation (e.g., whether it holds across all threat models, dataset types, or machine learning architectures) and any caveats to the membership inference attack analysis are not discussed in the available excerpt.
What different sources said
- arXiv cs.LGCenter
On Choosing the $\mu$ Parameter in Gaussian Differential Privacy
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